This study analyzes the possibility of utilizing artificial neural networks (ANNs) to characterize the drying kinetics of linden leaf samples during infrared drying (IRD) at different temperatures (50, 60, and 70 °C) with sample thicknesses between 0.210 mm and 0.230 mm. The statistical parameters were constructed using several thin-layer models and ANN techniques. The coefficient of determination (R²) and root mean square error (RMSE) were utilized to evaluate the appropriateness of the models. The effective moisture diffusivity ranged from 4.13 × 10⁻¹² m²/s to 5.89 × 10⁻¹² m²/s, and the activation energy was 16.339 kJ/mol. The applied Page, Midilli et al., Henderson and Pabis, logarithmic, and Newton models could sufficiently describe the kinetics of linden leaf samples, with R² values of >0.9900 and RMSE values of <0.0025. The ANN model displayed R² and RMSE values of 0.9986 and 0.0210, respectively. In addition, the ANN model made significantly accurate predictions of the chemical properties of linden of total phenolic content (TPC), total flavonoid content (TFC), DPPH, and FRAP, with values of R² of 0.9975, 0.9891, 0.9980, and 0.9854, respectively. The validation of the findings showed a high degree of agreement between the anticipated values generated using the ANN model and the experimental moisture ratio data. The results of this study suggested that ANNs could potentially be applied to characterize the drying process of linden leaves and make predictions of their chemical contents.

En tr This study was conducted to determine the effects of nuts per cluster (NPC) and fruit stem lengths (FSL) on the fruit detachment force/husky hazelnut fruit weight (FDF/HFW) ratio in a hazelnut cultivar (Yomra cv.) at the different maturity times of fruit ripening (MTFR). In total, 63 hazelnut limbs allocated randomly into 21 experimental groups in a 3x7 factorial arrangements to evaluate the effect of maturity times of fruit ripening (MTFR), NPC and MTFRxNPC interaction on FSL, the fruit detachment force (FDF), husky fruit weight and FDF to W ratio. Three different maturity times of fruit ripening were determined for measurement the FSL, fruit detachment forces and husky hazelnut weights. The first maturity time was chosen as August 28th which is indicating the approximately half of the matured hazelnuts (immediately at beginning of maturity, no delay, IH). The second maturity time (September 7th) includes the term which nearly all the hazelnuts were matured (10 d delay, 10DM). The natural fruit dropping time (September 15th) was evaluated as third maturity time (18 d delay, 18DM). NPC were classified as single, double, triple, four, five, six and seven nuts per cluster. The FDF of IH were higher (P=0.016) than that of 18DM, while the FDF to W ratio of IH was higher (P<0.001) than that of 10DM and 18DM. The increase in NPC was increased dramatically the FDF and HFW (P<0.001), but decreased the FDF to W ratio. The seven nuts per husky had higher FDF and HFW values compared to other NPC, whereas the single nut per cluster had lower the FDF and HFW values, and higher values the FDF to HFW ratio compared to triple, four, five and six nuts per cluster. The relationships between the NPC or FSL and other studied variables were significant. In conclusion, to delay the MTFR decreased the FDF and the increase in NPC improved the FDF and HFW and as a result decreased the FDF to W ratio in hazelnut Yomra cv. fruit detachment force, husky fruit weight, maturity time of fruit ripening. Bu çalışma; farklı olgunlaşma dönemlerinde çotanaktaki fındık sayısının (ÇFS) ve meyve sapı uzunluklarının (MSU), fındıkta (Yomra çeşidi) meyve kopma kuvveti/zuruflu fındık ağırlığı oranı (MKK/ZFA) üzerine etkilerinin belirlenmesi amacıyla yapılmıştır. Meyve olgunlaşma zamanı (MOZ), ÇFS ve MOZ x ÇFS etkileşiminin MSU, ZFA ve MSU/ZFA oranı üzerindeki etkisini değerlendirmek için 63 adet fındık dalı 3 (olgunlaşma zamanı)x7 (çotanaktaki fındık sayısı) faktöriyel düzende olmak üzere 21 deneysel gruba rastgele ayrılmıştır. Meyve sapı uzunluklarının (MSU), meyve kopma kuvvetlerinin (MKK) ve zuruflu meyve ağırlıklarının (ZFA) ölçümü için üç farklı olgunlaşma dönemi belirlenmiştir. Birinci olgunlaşma dönemi, meyvelerin yaklaşık yarısının olgunlaştığı dönem (28 Ağustos 2010; olgunlaşmanın hemen başında, IH) olarak seçilmiştir. İkinci dönem, hemen hemen tüm fındıkların olgunlaştığı dönemi (7 Eylül 2010, 10 gün sonra, 10H) içermektedir. Üçüncü olgunlaşma dönemi ise, fındıkların doğal olarak yere dökülmeye başladığı dönem olarak değerlendirilmiştir (15 Eylül 2010, 18 gün sonra, 18H). ÇFS ise tekli, ikili, üçlü, dörtlü, beşli, altılı ve yedili çotanak olarak sınıflandırılmıştır. Birinci olgunlaşma döneminde (IH) elde edilen meyve kopma kuvveti (MKK), üçüncü olgunlaşma döneminden (18H) daha yüksek (P=0.016) bulunmuştur. Ayrıca, birinci olgunlaşma döneminde (IH) elde edilen meyve kopma kuvveti/zuruflu meyve ağırlığı (MKK/ZFA) oranı hem ikinci olgunlaşma dönemi (10H), hem de üçüncü olgunlaşma döneminden (18H) daha yüksek bulunmuştur (P<0.001). ÇFS’deki artış, hem MKK hem de ZFA değerlerini arttırmış ancak, MKK/ZFA oranını düşürmüştür (P<0.001). Yedili çotanak, diğer çotanaklar ile karşılaştırıldığında daha yüksek FKK ve ZFA oluştururken; tekli çotanak üçlü, dörtlü, beşli ve altılı çotanaklara göre bu parametreler bakımından daha düşük değerlere sahip olmuş ve daha yüksek MKK/ZFA oranı oluşturmuştur. ÇFS veya MSU ile diğer değişkenler arasındaki ilişkiler önemli bulunmuştur. Sonuç olarak; Yomra fındık çeşidindeki MOZ gecikmesi, FKK değerini azaltırken; ÇFS artışı, FKK ve ZFA değerlerini iyileştirmiş ve sonuçta MKK/ZFA oranını düşürmüştür.

This study was completed with the aim of determining work efficiencies, labour requirement, and total cost of second fodder corn silage agricultural enterprises on flat ground in Bafra district of Samsun province. According to the results, total variable and fixed costs were calculated 2827.80 TL ha-1 and 4224.60 TL ha-1. The shares of variable and fixed costs in total production costs were determined 40.10% and 59.90%, respectively. Land hire had 24.70% as the highest share in fixed costs while the highest share for variable costs were observed in fertilization (10.30%) and spraying (7.00%). The highest and the lowest unit of labour requirements (units of human labour power, h ha–1) were obtained for harvesting processes (4.28 h ha-1) and spraying (2.35 h ha-1), respectively. In terms of work efficiencies, the best value was determined (0.53 ha h-1) for the transporting processes.

This study aimed to evaluate the spray performance of an agricultural unmanned aerial vehicle (UAV) equipped with four fan-type hydraulic nozzles (LU 120-01; IDK 100-01; AD 90-01-C; and ST 01-110) on target horizontal and vertical planes. Analysis of variance (ANOVA) revealed statistically significant differences (p < 0.05) among nozzle types and sampling heights in terms of droplet coverage and distribution uniformity. The LU 120-01 nozzle achieved the highest mean horizontal surface coverage (6.40%), while the IDK 100-01 nozzle produced the highest vertical coverage (1.89%) and exhibited more uniform droplet distribution. LU nozzles generally performed better at lower heights, whereas ST 01-110 nozzles achieved superior coverage at higher altitudes. Overall, the results highlight the necessity of evaluating horizontal and vertical deposition patterns in UAV-based pesticide applications to ensure uniform and effective chemical delivery across crop canopies.

Understanding how the classification and identification of biological species can evaluate improvements in newly developed cultivars, including chestnuts (Castanea sativa), is crucial for product processing and equipment design. To evaluate this, in the present study artificial neural networks (ANNs) were used to characterize four hybrid chestnut cultivars (Macit 55, Akyüz, and Ali Nihat registered in Türkiye and Bouche de Betizac registered in France). A backpropagation neural networks algorithm was used in the ANN approach based on nine physical properties. These properties included shelled nut weight and volume, sphericity, geometric mean diameter, bulk density, surface area, true density, porosity, and length, which can be deemed for classifying the cultivars. The ANN model was composed of input (9), hidden (6-5), and output (1) layers. In the hidden layers and output layer, tansig transfer and linear transfer functions were used, respectively. The R2 value for the test and training data was 0.99999 (RMSE = 0.000083 and 0.0023, respectively). The relative error (ε) between the real values and the estimated values was 0.079%. In conclusion, the ANN approach is able to discriminate among Macit 55, Akyüz, Ali Nihat, and Bouche de Betizac accessions based on the values of R2 and ε.